Multiple Kernel Learning for Sparse Representation-Based Classification

Shrivastava, Ashish; Patel, Vishal M.; Chellappa, Rama

doi:10.1109/tip.2014.2324290

Cited by 89 publications

(43 citation statements)

References 39 publications

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“…As a result, we used a polynomial kernel of degree 4 in our experiments. Several methods have been proposed in the literature for optimizing the choice of kernel and kernel parameters such as cross validation and multiple kernel learning (Shrivastava et al 2014a). However, these methods tend to make the optimization problem very complex and time consuming.…”

Section: Resultsmentioning

confidence: 99%

Generalized Dictionaries for Multiple Instance Learning

et al. 2015

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We present a multi-class multiple instance learning (MIL) algorithm using the dictionary learning framework where the data is given in the form of bags. Each bag contains multiple samples, called instances, out of which at least one belongs to the class of the bag. We propose a noisy-OR model and a generalized mean-based optimization framework for learning the dictionaries in the feature space. The proposed method can be viewed as a generalized dictionary learning algorithm since it reduces to a novel discriminative dictionary learning framework when there is only one instance in each bag. Various experiments using popular vision-related MIL datasets as well as the UNBC-McMaster Pain Shoulder Archive database show that the proposed method performs significantly better than the existing methods.

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Section: Resultsmentioning

confidence: 99%

Generalized Dictionaries for Multiple Instance Learning

et al. 2015

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“…There is a very rich literature on image classification including methods based on bag of word [1,2], Sparse representation [3][4][5][6][7], and Deep learning [8][9][10]. We should point out that nonlinear classifiers, including kernel based ones, have gained more attention due to their high performance compared to linear classifiers [5,7,9].…”

Section: Introductionmentioning

confidence: 99%

“…We should point out that nonlinear classifiers, including kernel based ones, have gained more attention due to their high performance compared to linear classifiers [5,7,9].…”

Section: Introductionmentioning

confidence: 99%

A feature fusion based localized multiple kernel learning system for real world image classification

Zamani

Jamzad

2017

J Image Video Proc.

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Real-world image classification, which aims to determine the semantic class of un-labeled images, is a challenging task. In this paper, we focus on two challenges of image classification and propose a method to address both of them simultaneously. The first challenge is that representing images by heterogeneous features, such as color, shape and texture, helps to provide better classification accuracy. The second challenge comes from dissimilarities in the visual appearance of images from the same class (intra class variance) and similarities between images from different classes (inter class relationship). In addition to these two challenges, we should note that the feature space of real-world images is highly complex so they cannot be linearly classified. The kernel trick is efficacious to classify them. This paper proposes a feature fusion based multiple kernel learning (MKL) model for image classification. By using multiple kernels extracted from multiple features, we address the first challenge. To provide a solution for the second challenge, we use the idea of a localized MKL by assigning separate local weights to each kernel. We employed spatial pyramid match (SPM) representation of images and computed kernel weights based on Χ 2 kernel. Experimental results demonstrate that our proposed model has achieved promising results.

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“…However, in many practical applications, linear representations are not able to represent the non-linear structures of data. To address this issue, many efforts have been devoted to developing kernel sparse representation classification (KSRC) [23]- [27]. Differing from those methods that find sparse representation coefficients in the original space, these kernel-based methods first map the original data into a high-dimensional feature space, and then learn sparse representation in the obtained kernel space.…”

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Robust Face Recognition With Kernelized Locality-Sensitive Group Sparsity Representation

Tan

Sun²,

Chan

et al. 2017

IEEE Trans. on Image Process.

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Abstract-In this paper, a novel joint sparse representation method is proposed for robust face recognition. We embed both group sparsity and kernelized locality-sensitive constraints into the framework of sparse representation. The group sparsity constraint is designed to utilize the grouped structure information in the training data. The local similarity between test and training data is measured in the kernel space instead of the Euclidian space. As a result, the embedded nonlinear information can be effectively captured, leading to a more discriminative representation. We show that, by integrating the kernelized localsensitivity constraint and the group sparsity constraint, the embedded structure information can be better explored, and significant performance improvement can be achieved. On the one hand, experiments on the ORL, AR, extended Yale B, and LFW data sets verify the superiority of our method. On the other hand, experiments on two unconstrained data sets, the LFW and the IJB-A, show that the utilization of sparsity can improve recognition performance, especially on the data sets with large pose variation.

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Multiple Kernel Learning for Sparse Representation-Based Classification

Cited by 89 publications

References 39 publications

Generalized Dictionaries for Multiple Instance Learning

Generalized Dictionaries for Multiple Instance Learning

A feature fusion based localized multiple kernel learning system for real world image classification

Robust Face Recognition With Kernelized Locality-Sensitive Group Sparsity Representation

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